Machine learning allergy risk diagnosis determination

ABSTRACT

A system for improving a machine learning allergy diagnosis process is provided. The system implements a method that includes identifying databases comprising data identifying menu items served by food establishments. Allergy data defining a food products associated with potential allergens is analyzed in real time and in response, baseline allergen indication software code for defining an allergen baseline indicator is generated. Ingredients of the menu items are analyzed with respect to the allergy data and allergen baseline levels for the ingredients determined. An allergen baseline level for each menu item is determined and an allergen alert software application for alerting a user with respect to each overall allergen assessment is generated.

FIELD

The present invention relates generally to a method for monitoringallergy based conditions with respect to a user and in particular to amethod and associated system for improving allergen alert softwaretechnology associated with enabling sensors for detecting biometriclevels of a user and executing associated actions for automaticallygenerating software code instructions.

BACKGROUND

Accurately determining user issues typically includes an inaccurateprocess with little flexibility. Controlling devices associated withpreventing the user issues may include a complicated process that may betime consuming and require a large amount of resources. Accordingly,there exists a need in the art to overcome at least some of thedeficiencies and limitations described herein above.

SUMMARY

A first aspect of the invention provides a machine learning allergy riskdiagnosis improvement method comprising: identifying, by a processor ofa server hardware device, a plurality of databases comprising dataidentifying menu items served by a plurality of food establishments;first analyzing in real time, by the processor executing naturallanguage software code, allergy data defining a plurality of foodproducts associated with a plurality of potential allergens; generating,by the processor based on results of the analyzing, baseline allergenindication software code for defining an allergen baseline indicator;second analyzing in real time, by the processor executing the naturallanguage software code and the baseline allergen indication softwarecode, ingredients of the menu items with respect to the allergy data;determining, by the processor based on results of the second analyzingin real time, allergen baseline levels for the ingredients of the menuitems; determining, by the processor, an overall allergen assessment foreach menu item of the menu items; and generating, by the processor, anallergen alert software application for alerting users with respect toeach the overall allergen assessment.

A second aspect of the invention provides computer program product,comprising a computer readable hardware storage device storing acomputer readable program code, the computer readable program codecomprising an algorithm that when executed by a processor of a serverhardware device implements a machine learning allergy risk diagnosisimprovement method, the method comprising: identifying, by theprocessor, a plurality of databases comprising data identifying menuitems served by a plurality of food establishments; first analyzing inreal time, by the processor executing natural language software code,allergy data defining a plurality of food products associated with aplurality of potential allergens; generating, by the processor based onresults of the analyzing, baseline allergen indication software code fordefining an allergen baseline indicator; second analyzing in real time,by the processor executing the natural language software code and thebaseline allergen indication software code, ingredients of the menuitems with respect to the allergy data; determining, by the processorbased on results of the second analyzing in real time, allergen baselinelevels for the ingredients of the menu items; determining, by theprocessor, an allergen baseline levels for each menu item of the menuitems; and generating, by the processor, an allergen alert softwareapplication for alerting users with respect to each the overall allergenassessment.

A third aspect of the invention provides server hardware devicecomprising a processor coupled to a computer-readable memory unit, thememory unit comprising instructions that when executed by the computerprocessor implements a machine learning allergy risk diagnosisimprovement method comprising: identifying, by the processor, aplurality of databases comprising data identifying menu items served bya plurality of food establishments; first analyzing in real time, by theprocessor executing natural language software code, allergy datadefining a plurality of food products associated with a plurality ofpotential allergens; generating, by the processor based on results ofthe analyzing, baseline allergen indication software code for definingan allergen baseline indicator; second analyzing in real time, by theprocessor executing the natural language software code and the baselineallergen indication software code, ingredients of the menu items withrespect to the allergy data; determining, by the processor based onresults of the second analyzing in real time, allergen baseline levelsfor the ingredients of the menu items; determining, by the processor, anallergen baseline levels for each menu item of the menu items; andgenerating, by the processor, an allergen alert software application foralerting users with respect to each the overall allergen assessment.

The present invention advantageously provides a simple method andassociated system capable of accurately determining user issues.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for improving machine learning allergy riskdiagnosis technology associated with enabling sensors for monitoringuser conditions and executing associated actions for automaticallyupdating software and notifying authorized personnel, in accordance withembodiments of the present invention.

FIG. 2 illustrates an algorithm detailing a process flow enabled by thesystem of FIG. 1 for improving machine learning allergy risk diagnosistechnology associated with enabling sensors for monitoring userconditions and executing associated actions for automatically updatingsoftware and notifying authorized personnel, in accordance withembodiments of the present invention.

FIG. 3 illustrates an internal structural view of the softwarecode/hardware structure of FIG. 1, in accordance with embodiments of thepresent invention.

FIG. 4 illustrates a computer system used by the system of FIG. 1 forimproving machine learning allergy risk diagnosis technology associatedwith enabling sensors for monitoring user conditions and executingassociated actions for automatically updating software and notifyingauthorized personnel, in accordance with embodiments of the presentinvention.

FIG. 5 illustrates a cloud computing environment, in accordance withembodiments of the present invention.

FIG. 6 illustrates a set of functional abstraction layers provided bycloud computing environment, in accordance with embodiments of thepresent invention.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 100 for improving machine learning allergyrisk diagnosis technology associated with enabling sensors formonitoring user conditions and executing associated actions forautomatically updating software and notifying authorized personnel, inaccordance with embodiments of the present invention. System 100 enablesa process for automatically generating software for providing rankingsfor restaurants based on a user's food allergy information (i.e.,comprising a potential for causing an allergic reaction). The generatedsoftware executes a process for receiving database retrieved supplychain data and applying natural language processing code to the supplychain data to identify user allergens and associated solution actions.

System 100 enables a process for automatically processing data (viaexecution of machine learning code) simultaneously retrieved (in realtime) from multiple Web-scraped (i.e., an automated process implementedusing a bot or web crawler.) sources and combined with known userinformation to provide personalized restaurant and menu recommendations.Results of the processed data are presented (via a graphical userinterface (GUI)) in the form of a grading/rating system for effectivelycommunicating the convenience and safety of any given restaurantaccording to an individual's dietary needs. Additionally, a personalizedsafety rating is automatically generated based on a comprehensiveallergen analysis of a restaurant and a sensitivity profile of thediner(s).

System 100 of FIG. 1 includes a server hardware device 104 (i.e.,specialized hardware device), a mobile hardware device 105 (i.e.,specialized hardware device), food establishment systems 112, emergencyservices systems 115, and a database system 107 a. . . 107 ninterconnected through a network 117 a, 117 b, and 117 c. Serverhardware device 104 includes specialized circuitry 125 a (that mayinclude specialized software), encryption code 119 (for encrypting anyoutput) and software code 121. Mobile hardware device 105 may include apersonal food restriction device provided and registered to all userswith food restrictions. Mobile hardware device 105 may be Bluetooth (ornear field communication, etc.) enabled to provide connectivity to afood establishment systems 112. Mobile hardware device 105 provides aregistered ID linking to a central profile storing a user's defined foodrestrictions. Mobile hardware device 105 includes specialized circuitry125 b (that may include specialized software), calibrationsoftware/hardware, sensors 110 a. . . 11On, and administering hardware134. Sensors 110 a. . . 11On may include any type of internal orexternal biomedical sensors including, inter alia, a heart rate monitor,a blood pressure monitor, a temperature sensor, a pulse rate monitor, anultrasonic sensor, an optical sensor, a video retrieval device, humiditysensors, etc. Administering hardware 134 may comprise devices forautomatically administering an allergic reaction substance to a user inresponse to a detected allergy issue. For example, administeringhardware 134 may include a controller for activating a solenoid forinitializing an epinephrine administering device for administeringepinephrine to a user experiencing an allergic reaction. Additionally,administering hardware 134 may comprise automated medical devices andassociated automated controllers for automatically modifying biomedicalattributes of the user. Calibration software/hardware 132 may includespecialized testing circuitry/logic. Server hardware device 104, mobilehardware device 105, and database system 107 a. . . 107 n may eachcomprise an embedded device. An embedded device is defined herein as adedicated device or computer comprising a combination of computerhardware and software (fixed in capability or programmable) specificallydesigned for executing a specialized function. Programmable embeddedcomputers or devices may comprise specialized programming interfaces. Inone embodiment, server hardware device 104 , mobile hardware device 105,and database system 107 a. . . 107 n may each comprise a specializedhardware device comprising specialized (non-generic) hardware andcircuitry (i.e., specialized discrete non- generic analog, digital, andlogic based circuitry) for (independently or in combination) executing aprocess described with respect to FIGS. 1-5. The specialized discretenon-generic analog, digital, and logic based circuitry may includeproprietary specially designed components (e.g., a specializedintegrated circuit, such as for example an Application SpecificIntegrated Circuit (ASIC) designed for only implementing an automatedprocess for improving machine learning medical issue determinationtechnology. Network 117 a, 117 b, and 117 c may include any type ofnetwork including, inter alia, a local area network, (LAN), a wide areanetwork (WAN), the Internet, a wireless network, etc. Alternatively,network 117 a, 117 b, and 117 c may include application programminginterfaces (API). Food establishment systems 112 may include a providerpresence device for communicating (via Bluetooth) with mobile hardwaredevice 105 to identify profiles determining food restrictions for agroup of users. The provider presence device continuously retrievesnotifications of restrictions and provides a self-servicehardware/software interface for the group to evaluate a foodestablishment's compatibility with the group's restrictions. Amembership to a group may be implemented based on a set proximitythreshold and augmented by providing a list of discovered devices of thegroup. Additionally, the provider presence device requires access to theInternet for connectivity with server hardware device 104.

The following implementation example (executed by system 100) describesa process for improving machine learning medical issue determinationtechnology with respect to executing continuously running software code(in real time) for gathering and profiling restaurants and associatedmenu content:

Potential database sources are identified for each restaurant in thedatabase. Data sources may include, inter alia, Web content supplied bya participating restaurant, supplier data associated with a restaurant,etc. Supply chain content (for each allergen source) is run (in realtime) through a natural language processor to locate products matching apopulated vocabulary of allergens. A resulting output is enabled toprovide a baseline allergen indication such as no allergen or allergen.Additionally, restaurant recipes are run through the natural languageprocessor to locate items that have been identified asallergen-containing in the supply chain content. If a supply chain hasnot set an allergen baseline, a baseline for the restaurant may be setto: Allergen or Allergen unknown. Menus are analyzed to identify (byitem) an allergen-level and for each menu item, a baseline is generatedat an item level: Allergen, No allergen, Allergen unknown, etc.Additionally (for each social media or news source discovered) allergyblogs, tweets, online reviews, medical reports, news articles, etc. areanalyzed to identify entries for a selected restaurant. The entries areparsed the content entries that are allergen related are identified. Allresults are combined in a weighted formula to identify a combinedpositive, negative, or neutral/no media weight. Additionally (asillustrated in the following logical risk table), weighted values may beused to provide an overall risk assessment for a specified restaurant inwith respect to a specified allergen(s).

Risk Table Positive media Negative media No media Allergen low risksevere risk high risk indicated indicated indicated No no risk generalrisk general risk allergen indicated indicated indicated Allergen lowrisk severe risk unknown unknown indicated indicated

The logical risk table is enabled to configure a memory structure (e.g.,a database) to improve database access retrieval speed with respect toretrieving data from specified portions of the database.

The following implementation example (executed by system 100) describesa process for improving machine learning medical issue determinationtechnology with respect to retrieving user information associated withuser sensitivities to specified allergens including a relative risk ofan allergen at a restaurant and menu item level:

A user downloads a mobile software application configured toautomatically integrate user information with central database storedrestaurant data to provide an individualized assessment of diningoptions. Additionally, the user creates one or more local profiles fordining. Creating the profile may include retrieving key allergy andsensitivity information, electronic medical data related to allergies,etc. The mobile software application is executed and a user selects ageographic location of interest to search for a restaurant. In response,the mobile software application analyzes a selected user profile withrespect to listed allergens for each local restaurant and presents aranked list based on determined allergen risk. Additionally, selecting auser profile may include executing an application for enabling acommunication link (e.g., a Bluetooth connection) to automaticallylocate additional mobile hardware devices for selecting user profilesfrom the additional mobile hardware devices via the application.

FIG. 2 illustrates an algorithm detailing a process flow enabled bysystem 100 of FIG. 1 for improving machine learning medical issuedetermination technology associated with enabling sensors for monitoringuser conditions and executing associated actions for automaticallyupdating software and notifying authorized personnel, in accordance withembodiments of the present invention. Each of the steps in the algorithmof FIG. 2 may be enabled and executed in any order by a computerprocessor(s) executing computer code. Additionally, each of the steps inthe algorithm of FIG. 2 may be enabled and executed in combination byserver hardware device 104 and mobile hardware device 105. In step 200,databases are identified. The databases include data identifying menuitems served by a plurality of food establishments. In step 202, allergydata is analyzed in real time via execution of natural language softwarecode. The allergy data defines a plurality of food products associatedwith a plurality of potential allergens. In step 204, baseline allergenindication software code is generated based on results of the analysisof step 202. The baseline allergen indication software code isconfigured to define an allergen baseline indicator. In step 208,ingredients of the menu items are analyzed in real time with respect tothe allergy data via execution of natural language software code and thebaseline allergen indication software code. In step 210, allergenbaseline levels for the ingredients are determined based on results ofstep 208. In step 212, an overall allergen assessment for each menu itemis determined. In step 214, an allergen alert software application foralerting a user with respect to each overall allergen assessment isgenerated. In step 217, the allergen alert software application isoptionally encrypted resulting in an encrypted allergen alert softwareapplication. Additionally, the encrypted allergen alert softwareapplication is transmitted to a plurality of mobile hardware devices. Instep 218, the encrypted (or decoded) allergen alert software applicationis executed via a mobile hardware device. In step 220, allergyinformation describing allergy issues of the user is retrieved andanalyzed with respect to each overall allergen assessment. In step 224,a ranked list of the plurality of food establishments is generated basedon results of step 220. The ranked list is presented to the user and theuser is automatically directed to a food establishment of the rankedlist of food establishments. In step 228, biometric levels of the userare automatically detected via a plurality of sensor devices of themobile hardware device. Additionally, allergy related symptoms of theuser are automatically determined based on the biometric levels detectedvia the sensors. The allergy related symptoms indicate an initial stageof an allergic reaction with respect to a first food item being consumedby the user. Alternatively, ingredient information describing specifiedingredient issues medically affecting a user may be retrieved and it maybe determined that specific ingredients (served by the foodestablishment of the ranked list of food establishments) may beassociated with the specified ingredient issues medically affecting theuser. In step 232, a warning is transmitted to a hardware device of thefood establishment. The warning indicates the initial stage of theallergic reaction thereby presenting feedback information for providingto an emergency services server. Alternatively, a warning indicatingthat the specified ingredients are associated with the specifiedingredient issues. In step 234, the allergen alert software applicationis automatically updated based on the biometric levels of the user. Instep 236, actions associated with the allergy related symptoms areexecuted. The action may include, inter alia:

-   -   1. Automatically signaling an emergency services server such        that an ambulance is automatically dispatched to a location of        the food establishment.    -   2. Automatically instructing the user to self-administer        epinephrine to a specified body portion and automatically        administering (in response to detecting that the user is placing        an anti-allergen administering device of the mobile hardware        device in contact with the specified body portion) the        epinephrine to the specified body portion of the user.

In step 238, a calibration error of at least one sensor device isdetected and automatically calibrated via a hardware calibration or asoftware calibration process.

FIG. 3 illustrates an internal structural view of software code/hardwarestructure 121 of FIG. 1, in accordance with embodiments of the presentinvention. Software code/hardware structure 121 includes communicationcontrollers 302 (i.e., specialized hardware and software)communicatively linked to the following external endpoint systems: amobile hardware device 104 (in FIG. 1), supply chain source systems, andan Internet bot for scraping social media and menu descriptions.Software code/hardware structure 121 additionally includes a naturallanguage processing module 304, a social media module 310, a centralrating module 308, and a natural language module 314. Natural languageprocessing module 304 comprises specialized hardware and softwareconfigured to reduce supply chain information to constituent foodallergy risk factors. Social media module 310 comprises specializedhardware and software configured to distill social media posts into foodallergy indicators reflecting positive and negative sentiment andexperience. Central rating module 308 comprises specialized hardware andsoftware configured to combine supply chain source systems results withsocial media results to create overall risk ratings for each allergen.Natural language module 314 comprises specialized hardware and softwareconfigured to process menu descriptions to identify allergen indicatorsby identifying a description, accessing a lookup table containingrecipe-based ingredients with allergen ratings by ingredient, andassessing allergen risks related to the potential supply chain paths foreach ingredient. Natural language module 314 runs the following internalmodules in parallel: a continuous learning module and an additionalcontinuous learning module. The continuous learning module runs inparallel to identify supply chain sources for each ingredient andprovides contamination possibilities for each ingredient based on supplychain guarantees and parallel product production/packaging. Theadditional continuous learning module searches for recipes by dish nameto discover alternate recipes to assure that all possible risks areincluded on a menu item in the absence of the specific recipe used.Server hardware device 105 additionally provides access to registeredusers to manage individual profiles linked to mobile hardware device104.

FIG. 4 illustrates a computer system 90 (e.g., mobile hardware device104 and/or server hardware device 105 of FIG. 1) used by or comprised bythe system of FIG. 1 for improving machine learning medical issuedetermination technology associated with enabling sensors for monitoringuser conditions and executing associated actions for automaticallyupdating software and notifying authorized personnel, in accordance withembodiments of the present invention.

Aspects of the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module,” or “system.”

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing apparatus receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, device(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing device to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing device, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing device, and/or other devicesto function in a particular manner, such that the computer readablestorage medium having instructions stored therein comprises an articleof manufacture including instructions which implement aspects of thefunction/act specified in the flowchart and/or block diagram block orblocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing device, or other device tocause a series of operational steps to be performed on the computer,other programmable device or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable device, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The computer system 90 illustrated in FIG. 4 includes a processor 91, aninput device 92 coupled to the processor 91, an output device 93 coupledto the processor 91, and memory devices 94 and 95 each coupled to theprocessor 91. The input device 92 may be, inter alia, a keyboard, amouse, a camera, a touchscreen, etc. The output device 93 may be, interalia, a printer, a plotter, a computer screen, a magnetic tape, aremovable hard disk, a floppy disk, etc. The memory devices 94 and 95may be, inter alia, a hard disk, a floppy disk, a magnetic tape, anoptical storage such as a compact disc (CD) or a digital video disc(DVD), a dynamic random access memory (DRAM), a read-only memory (ROM),etc. The memory device 95 includes a computer code 97. The computer code97 includes algorithms (e.g., the algorithm of FIG. 2) for improvingmachine learning medical issue determination technology associated withenabling sensors for monitoring user conditions and executing associatedactions for automatically updating software and notifying authorizedpersonnel. The processor 91 executes the computer code 97. The memorydevice 94 includes input data 96. The input data 96 includes inputrequired by the computer code 97. The output device 93 displays outputfrom the computer code 97. Either or both memory devices 94 and 95 (orone or more additional memory devices Such as read only memory device96) may include algorithms (e.g., the algorithm of FIG. 2) and may beused as a computer usable medium (or a computer readable medium or aprogram storage device) having a computer readable program code embodiedtherein and/or having other data stored therein, wherein the computerreadable program code includes the computer code 97. Generally, acomputer program product (or, alternatively, an article of manufacture)of the computer system 90 may include the computer usable medium (or theprogram storage device).

In some embodiments, rather than being stored and accessed from a harddrive, optical disc or other writeable, rewriteable, or removablehardware memory device 95, stored computer program code 84 (e.g.,including algorithms) may be stored on a static, nonremovable, read-onlystorage medium such as a Read-Only Memory (ROM) device 85, or may beaccessed by processor 91 directly from such a static, nonremovable,read-only medium 85. Similarly, in some embodiments, stored computerprogram code 97 may be stored as computer-readable firmware 85, or maybe accessed by processor 91 directly from such firmware 85, rather thanfrom a more dynamic or removable hardware data-storage device 95, suchas a hard drive or optical disc.

Still yet, any of the components of the present invention could becreated, integrated, hosted, maintained, deployed, managed, serviced,etc. by a service supplier who offers to improve machine learningmedical issue determination technology associated with enabling sensorsfor monitoring user conditions and executing associated actions forautomatically updating software and notifying authorized personnel.Thus, the present invention discloses a process for deploying, creating,integrating, hosting, maintaining, and/or integrating computinginfrastructure, including integrating computer-readable code into thecomputer system 90, wherein the code in combination with the computersystem 90 is capable of performing a method for enabling a process forimproving machine learning medical issue determination technologyassociated with enabling sensors for monitoring user conditions andexecuting associated actions for automatically updating software andnotifying authorized personnel. In another embodiment, the inventionprovides a business method that performs the process steps of theinvention on a subscription, advertising, and/or fee basis. That is, aservice supplier, such as a Solution Integrator, could offer to enable aprocess for improving machine learning medical issue determinationtechnology associated with enabling sensors for monitoring userconditions and executing associated actions for automatically updatingsoftware and notifying authorized personnel. In this case, the servicesupplier can create, maintain, support, etc. a computer infrastructurethat performs the process steps of the invention for one or morecustomers. In return, the service supplier can receive payment from thecustomer(s) under a subscription and/or fee agreement and/or the servicesupplier can receive payment from the sale of advertising content to oneor more third parties.

While FIG. 4 shows the computer system 90 as a particular configurationof hardware and software, any configuration of hardware and software, aswould be known to a person of ordinary skill in the art, may be utilizedfor the purposes stated supra in conjunction with the particularcomputer system 90 of FIG. 4. For example, the memory devices 94 and 95may be portions of a single memory device rather than separate memorydevices.

Cloud Computing Environment

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A, 54B,54C and 54N shown in FIG. 5 are intended to be illustrative only andthat computing nodes 10 and cloud computing environment 50 cancommunicate with any type of computerized device over any type ofnetwork and/or network addressable connection (e.g., using a webbrowser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (see FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 89 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and for improving machine learning medicalissue determination technology associated with enabling sensors formonitoring user conditions and executing associated actions forautomatically updating software and notifying authorized personnel 96.

While embodiments of the present invention have been described hereinfor purposes of illustration, many modifications and changes will becomeapparent to those skilled in the art. Accordingly, the appended claimsare intended to encompass all such modifications and changes as fallwithin the true spirit and scope of this invention.

What is claimed is:
 1. A computer program product, comprising a computerreadable hardware storage device storing a computer readable programcode, said computer readable program code comprising an algorithm thatwhen executed by a processor of a server hardware device implements amachine learning allergy risk diagnosis improvement method, said methodcomprising: identifying, by said processor, a plurality of databasescomprising data identifying menu items served by a plurality of foodestablishments; executing, by said processor, a Web scraping processassociated with a simultaneous process for retrieving restaurant andmenu recommendations and supplier data for suppliers of restaurantsspecified in said restaurant and menu recommendations; simultaneouslyretrieving, by said processor in response to said executing said Webscraping process, said restaurant and menu recommendations;simultaneously retrieving, by said processor in response to saidexecuting said Web scraping process, social media data, allergy blogdata, and medical report data associated with said restaurants; firstanalyzing in real time, by said processor executing natural languagesoftware code with respect to said restaurant and menu recommendations,said social media data, said allergy blog data, and said medical reportdata, allergy data defining a plurality of food products associated witha plurality of potential allergens; generating, by said processor basedon results of said analyzing, baseline allergen indication software codefor defining an allergen baseline indicator; running, by said processor,restaurant recipes, of said restaurants specified in said restaurant andmenu recommendations, through said natural language software code tolocate items identified as allergen-containing within supply chaincontent of said suppliers; second analyzing in real time, by saidprocessor executing said natural language software code and saidbaseline allergen indication software code with respect to results ofsaid running, ingredients of said menu items with respect to saidallergy data; determining, by said processor based on results of saidsecond analyzing in real time, allergen baseline levels for saidingredients of said menu items; determining, by said processor, anallergen baseline level for each menu item of said menu items;generating, by said processor, an overall allergen assessment associatedwith each said allergen baseline level; generating, by said processor,an allergen alert software application for alerting users with respectto each said overall allergen assessment associated with each saidallergen baseline level; generating, by said processor, a logicalallergen risk table associated with a weighted risk assessment for saidplurality of food establishments with respect to each said overallallergen assessment, wherein said logical allergen risk table increasesan access retrieval speed of said plurality of databases with respect toretrieving said data from specified portions of the plurality ofdatabases; automatically directing, by said processor based on a userselection, said user to a first food establishment causing said user toproceed to said first food establishment; enabling, by said processor, acommunication link of said mobile hardware device; locating, by saidprocessor via said communication link, additional mobile hardwaredevices comprising profiles associated with said food establishment;automatically detecting, by said processor via a plurality of sensordevices of said mobile hardware device, biometric levels of said user atsaid first food establishment, wherein said plurality of sensor devicescomprise a video retrieval device, a blood pressure monitor, atemperature sensor, and a pulse rate monitor for said automaticallydetecting said biometric levels; automatically determining, by saidprocessor based on said biometric levels of said user, allergy relatedsymptoms of said user indicating an initial stage of an allergicreaction with respect to a first food item being consumed by said user;enabling, by said processor in response to said indicating said initialstage of said allergic reaction, an administrating hardware structurecomprising automated medical devices and associated automatedcontrollers to automatically activate a solenoid for initializing,enabling and causing said automated medical devices, comprising anepinephrine administering device, to automatically administerepinephrine to said user; automatically modifying, by said processorenabling said automated medical devices and said associated automatedcontrollers of said administrating hardware structure, biometricattributes of said user with respect to said biometric levels;automatically detecting by said processor, a calibration error of saidvideo retrieval device, said blood pressure monitor, said temperaturesensor, and said pulse rate monitor; and automatically calibrating, bysaid processor, software and hardware of said video retrieval device,said blood pressure monitor, said temperature sensor, and said pulserate monitor.
 2. The computer program product of claim 1, wherein saidmethod further comprises: encrypting, by said processor, said allergenalert software application resulting in an encrypted allergen alertsoftware application; and transmitting, by said processor, saidencrypted allergen alert software application to a plurality of mobilehardware devices.
 3. The computer program product of claim 1, whereinsaid method further comprises: transmitting, by said processor, saidallergen alert software application to a mobile hardware device;executing, by said processor via said mobile hardware device, saidallergen alert software application; retrieving, by said processor froma user of said mobile hardware device, allergy information describingallergy issues of said user; third analyzing, by said processorexecuting said allergen alert software application, said allergyinformation with respect to each said overall allergen assessment;generating, by said processor based on results of said third analyzing,a ranked list of said plurality of food establishments; presenting, bysaid processor to said user via said mobile hardware device, said rankedlist.
 4. The computer program product of claim 3, wherein said methodfurther comprises: transmitting, by said processor to a device of saidfirst food establishment, a warning indicating said initial stage ofsaid allergic reaction of said user thereby presenting feedbackinformation for providing to an emergency services server.
 5. Thecomputer program product of claim 1, wherein said method furthercomprises: automatically updating, by said processor based on saidbiometric levels of said user, said allergen alert software application.6. The computer program product of claim 1, wherein said method furthercomprises: retrieving, by said processor from a user of said mobilehardware device, ingredient information describing specified ingredientissues medically affecting said user; and automatically presenting bysaid processor to said user based on said ingredient information, awarning indicating specified ingredients of said ingredients of saidmenu items associated with said specified ingredient issues.
 7. Thecomputer program product of claim 1, wherein said method furthercomprises: automatically signaling, by said processor based on saidallergy related symptoms of said user, an emergency services server suchthat an ambulance is automatically dispatched to a location of saidfirst food establishment.
 8. A server hardware device comprising aprocessor coupled to a computer-readable memory unit, said memory unitcomprising instructions that when executed by the computer processorimplements a machine learning allergy risk diagnosis improvement methodcomprising: identifying, by said processor, a plurality of databasescomprising data identifying menu items served by a plurality of foodestablishments; executing, by said processor, a Web scraping processassociated with a simultaneous process for retrieving restaurant andmenu recommendations and supplier data for suppliers of restaurantsspecified in said restaurant and menu recommendations; simultaneouslyretrieving, by said processor in response to said executing said Webscraping process, said restaurant and menu recommendations;simultaneously retrieving, by said processor in response to saidexecuting said Web scraping process, social media data, allergy blogdata, and medical report data associated with said restaurants; firstanalyzing in real time, by said processor executing natural languagesoftware code with respect to said restaurant and menu recommendations,said social media data, said allergy blog data, and said medical reportdata, allergy data defining a plurality of food products associated witha plurality of potential allergens; generating, by said processor basedon results of said analyzing, baseline allergen indication software codefor defining an allergen baseline indicator; running, by said processor,restaurant recipes, of said restaurants specified in said restaurant andmenu recommendations, through said natural language software code tolocate items identified as allergen-containing within supply chaincontent of said suppliers; second analyzing in real time, by saidprocessor executing said natural language software code and saidbaseline allergen indication software code with respect to results ofsaid running, ingredients of said menu items with respect to saidallergy data; determining, by said processor based on results of saidsecond analyzing in real time, allergen baseline levels for saidingredients of said menu items; determining, by said processor, anallergen baseline level for each menu item of said menu items;generating, by said processor, an overall allergen assessment associatedwith each said allergen baseline level; generating, by said processor,an allergen alert software application for alerting users with respectto each said overall allergen assessment associated with each saidallergen baseline level; generating, by said processor, a logicalallergen risk table associated with a weighted risk assessment for saidplurality of food establishments with respect to each said overallallergen assessment, wherein said logical allergen risk table increasesan access retrieval speed of said plurality of databases with respect toretrieving said data from specified portions of the plurality ofdatabases; automatically directing, by said processor based on a userselection, said user to a first food establishment causing said user toproceed to said first food establishment; enabling, by said processor, acommunication link of said mobile hardware device; locating, by saidprocessor via said communication link, additional mobile hardwaredevices comprising profiles associated with said food establishment;automatically detecting, by said processor via a plurality of sensordevices of said mobile hardware device, biometric levels of said user atsaid first food establishment, wherein said plurality of sensor devicescomprise a video retrieval device, a blood pressure monitor, atemperature sensor, and a pulse rate monitor for said automaticallydetecting said biometric levels; automatically determining, by saidprocessor based on said biometric levels of said user, allergy relatedsymptoms of said user indicating an initial stage of an allergicreaction with respect to a first food item being consumed by said user;enabling, by said processor in response to said indicating said initialstage of said allergic reaction, an administrating hardware structurecomprising automated medical devices and associated automatedcontrollers a controller to automatically activate a solenoid forinitializing, enabling and causing said automated medical devices,comprising an epinephrine administering device, to automaticallyadminister epinephrine to said user; automatically modifying, by saidprocessor enabling said automated medical devices and said associatedautomated controllers of said administrating hardware structure,biometric attributes of said user with respect to said biometric levels;automatically detecting by said processor, a calibration error of saidvideo retrieval device, said blood pressure monitor, said temperaturesensor, and said pulse rate monitor; and automatically calibrating, bysaid processor, software and hardware of said video retrieval device,said blood pressure monitor, said temperature sensor, and said pulserate monitor.